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Crop Science Abstract - Turfgrass Science

An Enhanced Method of Tracking Divot Recovery in Turfgrass


This article in CS

  1. Vol. 51 No. 5, p. 2194-2198
    Received: Nov 14, 2010

    * Corresponding author(s): jkk@ufl.edu
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  1. Bradley T. Williamsa,
  2. Jason K. Kruse *a,
  3. J. Bryan Unruhb and
  4. Jerry B. Sartainc
  1. a Dep. of Environmental Horticulture, Univ. of Florida, Gainesville, FL 32611
    b J.B. Unruh, Dep. of Environmental Horticulture, Univ. of Florida, West Florida Research and Education Center, Milton, FL 32583
    c Dep. of Soil and Water Science, Univ. of Florida, Gainesville, FL 32611 Received 14 Nov. 2010


Divot recovery research has shown to have a great deal of resulting variability making it difficult to draw any solid conclusions about responses to differences among species, cultivars, or cultural practices. Standard divot analysis of the white divot (SDA) uses photos of a divot over time and digital image analysis (DIA) software to calculate percent recovery based on the number of green pixels in the photo. This method, however, neglects to take into account any nongreen pixels outside of the divoted area that result from external factors. A new method of analysis was developed using pink colored sand to fill divots and altering the macro to determine the percentage of pink pixels in the photo. Two divots were created per plot in bermudagrass [Cynodon dactylon (L.) Pers.]. One divot was backfilled with United States Golf Association spec white topdressing sand and analyzed using the SDA method. The other divot was backfilled with pink colored sand and analyzed using both the standard divot analysis of the pink divot (SDAP) method as well as the pink sand analysis (PSA) method. Each divot was also visually analyzed for percent recovery. Results indicated that the PSA method increased precision when compared with standard methods. At the time when divots were visually determined to be fully recovered, SDA and SDAP reported 60 to 70% recovery, whereas PSA reported recovery above 99%. Pink sand analysis reduced variability, as validated by a reduction in standard errors and increased R2 values, and increased the speed of analysis.

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